Dynamic network resource allocation method based on network slicing

ABSTRACT

A dynamic network resource allocation method based on network slicing is provided. A historical resource demand dataset of an accessed network slice is inputted into a first neural network for training. Based on a trained first neural network and the historical resource demand of the accessed network slice, a resource demand prediction information corresponding to the accessed network slice in a first prediction time period is determined. Resources are pre-allocated to the accessed network slice based on the resource demand prediction information, and resources are allocated to the accessed network slice when the first prediction time period arrives. In this way, the service provider can reasonably allocate network resources for network slices without violating the SLA, thus avoiding the waste of network resources.

CROSS REFERENCE TO THE RELATED APPLICATIONS

This application is based upon and claims priority to Chinese PatentApplication No. 202111004167.1, filed on Aug. 30, 2021, the entirecontents of which are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the technical field of networkresource allocation and, specifically, to a dynamic network resourceallocation method based on network slicing.

BACKGROUND

As one of the key technologies of 5G (5th Generation MobileCommunication Technology), network slicing can create and maintainmultiple independent customized logical networks based on the sharedphysical network infrastructure. Dedicated network slices tailored fordifferent types of 5G application scenarios improve heterogeneity,flexibility, scalability, and profitability of the network, as well asthe security of future network services.

Network resources of network slices are dynamically changed in real-timeduring actual application. In the prior art, the network resources ofnetwork slices can only be allocated through static allocation due tothe limitation of the Service Level Agreement (SLA). The SLA is amutually agreed contract between the service provider and the user orbetween the service providers to ensure the performance and reliabilityof services with specified overheads. In the static resource allocationmethod, the network slices occupy a large amount of unnecessary networkresources, which leads to the waste of network resources. In addition,because the network slices always occupy excessive network resources,network resources allocated by the system to new network slices arereduced, resulting in a small capacity of the system.

Therefore, how to help the service providers avoid waste of networkresources through reasonably allocating network resources for networkslices without violating the SLA is a technical problem to be resolvedby those skilled in the art.

SUMMARY

The purpose of the present disclosure is to resolve the problem that theexisting technology cannot reasonably allocate network resources fornetwork slices, resulting in the waste of network resources, andproposes a dynamic network resource allocation method based on networkslicing.

The present disclosure provides the following technical solutions: Adynamic network resource allocation method based on network slicingincludes:

-   S1: inputting a historical resource demand dataset of an accessed    network slice into a first neural network for training;-   S2: determining, based on a trained first neural network and the    historical resource demand of the accessed network slice, resource    demand prediction information corresponding to the accessed network    slice in a first prediction time period; and-   S3: pre-allocating resources to the accessed network slice based on    the resource demand prediction information and allocating resources    to the accessed network slice when the first prediction time period    arrives.

Further, the resource demand prediction information specificallyincludes a predicted node resource quantity and a predicted linkresource quantity.

Further, the step of pre-allocating resources to the accessed networkslice in the step S3 specifically includes determining a pre-allocatednode resource quantity and a pre-allocated link resource quantity of theaccessed network slice in the first prediction time period according tothe following formulas:

${\overset{˙}{d}}_{i}^{(t)} = \gamma_{d}{\hat{d}}_{i}^{(t)},{\overset{˙}{l}}_{i}^{(t)} = \gamma_{l}{\hat{l}}_{i}^{(t)},\forall i \in \lbrack {1,n_{t}} \rbrack,$

where

$\gamma_{d} = \{ {\begin{matrix}\begin{matrix}{1 + {\gamma^{\prime}}_{d},if{\sum{{}_{i = 1}^{n_{t}}{\gamma^{\prime}}_{d}{\hat{d}}_{i}^{(t)} < D}}} \\\begin{matrix}{\frac{D}{\Sigma_{i = 1}^{n_{t}}{\hat{d}}_{i}^{(t)}},} & {otherwise}\end{matrix}\end{matrix}\end{matrix}\text{and}\gamma_{l} =} )\{ \begin{matrix}{1 + {\gamma^{\prime}}_{l},if{\sum{{}_{i = 1}^{n_{t}}{\gamma^{\prime}}_{l}{\hat{l}}_{i}^{(t)} < L}}} \\\begin{matrix}{\frac{L}{\Sigma_{i = 1}^{n_{t}}{\hat{l}}_{i}^{(t)}},} & {otherwise}\end{matrix}\end{matrix} )$

${\overset{˙}{d}}_{i}^{(t)}$

is a pre-allocated node resource quantity of an accessed network slice iin a first prediction time period t.

${\overset{˙}{l}}_{i}^{(t)}$

is a pre-allocated link resource quantity of the accessed network slicei in the first prediction time period t.

d̂_(i)^((t))

is a predicted node resource quantity of the accessed network slice i inthe first prediction time period t.

l̂_(i)^((t))

is a predicted link resource quantity of the accessed network slice i inthe first prediction time period t. n_(t) is a quantity of accessednetwork slices in a system in the first prediction time period t. γ_(d)≥ 1 represents node resource redundancy. γ_(l) ≥ 1 represents linkresource redundancy. γ'_(d) ≥ 0 and y'_(l) ≥ 0 are offsets ofcorresponding predicted values. D is a total node resource quantity ofthe system. L is a total link resource quantity of the system.

Further, the method further includes the following steps:

-   A1: receiving access requests of all to-be-accessed network slices    in real-time and storing all the access requests in a request queue;-   A2: determining a total node resource quantity and a total link    resource quantity that are occupied by all accessed network slices    in a second prediction time period; and-   A3: when a preset access time point arrives, sequentially deciding    whether to allow the access requests in the request queue in the    second prediction time period; if yes, allowing a corresponding    access request when the second prediction time period arrives; and    if not, continuing to decide whether to allow a next access request    until all the access requests in the request queue are completely    decided.

Further, the step of determining the total node resource quantity andthe total link resource quantity in the step A2 is specificallyperformed by using the following formulas:

${\hat{d}}_{sys}^{(T)} = \Sigma_{i = 1}^{n_{T}}\mspace{6mu}{\overset{˙}{d}}_{i}^{(T)},{\hat{l}}_{sys}^{(T)} = \Sigma_{i = 1}^{n_{T}}\mspace{6mu}{\overset{˙}{l}}_{i}^{(T)},\forall T \in \lbrack {T_{ac} + 1,T_{ac} + T_{sys}} \rbrack,$

where

d̂_(sys)^((T))

is the total node resource quantity,

l̂_(sys)^((T))

is the total link resource quantity, T_(sys) is a length of a systemresource demand prediction window,

${\overset{˙}{d}}_{i}^{(T)}$

and

${\overset{˙}{l}}_{i}^{(T)}$

are, respectively, a node resource quantity and a link resource quantityto be occupied by an accessed network slice i at a moment T in thesecond prediction time period [T_(ac) + 1, T_(ac) + T_(sys)], T_(ac) isthe preset access time point, and n_(T) is a quantity of accessednetwork slices in the system at the moment T in the second predictiontime period.

Further, all the network slices are classified into high-quality networkslices and low-quality network slices.

Further, the step of deciding whether to allow the access requests inthe second prediction time period in the step A3 is specificallyperformed by using the following formulas:

$\begin{array}{l}{\exists t_{start} \in \lbrack {T_{ac} + 1,T_{ac} + T_{adm}} \rbrack,\mspace{6mu}\forall T \in \lbrack {t_{start},t_{start} + T^{dur}} \rbrack,} \\{{\hat{d}}_{sys}^{(T)} + d_{max} \leq D,{\hat{l}}_{sys}^{(T)} +} \\{l_{max} \leq L,}\end{array}$

where t_(start) is an instantiation time point of a to-be-accessednetwork slice corresponding to the access request, T_(ac) is adecision-making time point,

d̂_(sys)^((T))

and

l̂_(sys)^((T))

are, respectively, a total node resource quantity and a total linkresource quantity that are occupied by all the network slices at themoment T in the second prediction time period, T_(adm) is an accesswindow dimension, T^(dur) is survival duration of the to-be-accessednetwork slice corresponding to the access request, and d_(max) andl_(max) are, respectively, an upper limit of allocable node resourcesand an upper limit of allocable link resources; if a user requests ahigh-quality network slice, then

⟨d_(max), l_(max)⟩ = ⟨d_(max)^(H), l_(max)^(H)⟩;

if the user requests a standard quality network slice, then

⟨d_(max), l_(max)⟩ = ⟨d_(max)^(S), l_(max)^(S)⟩; 

D is a total node resource quantity of the system, L is a total linkresource quantity of the system,

d_(max)^(H)

is an upper limit of node resources for the high-quality network slices,

l_(max)^(H)

is an upper limit of link resources for the high-quality network slices,

d_(max)^(S)

is an upper limit of node resources for standard quality network slices,and

l_(max)^(S)

is an upper limit of link resources for the standard quality networkslices.

Further, in the step A3, each time an access request is decided to beallowed in the second prediction time period, the total node resourcequantity and the total link resource quantity are updatedinstantaneously, and the next access request is decided after the totalnode resource quantity and the total link resource quantity are updated.

Further, the total node resource quantity and the total link resourcequantity are updated by using the following formulas:

d̂_(sys)^((TT)) = d̂_(sys)^((T)) + d_(max), l̂_(sys)^((TT)) = l̂_(sys)^((T)) + l_(max), ∀T  ∈ [t_(start), t_(start) + T^(dur)],

where

d̂_(sys)^((TT))

is an updated total node resource quantity,

l̂_(sys)^((TT))

is an updated total link resource quantity,

d̂_(sys)^((T))

is the total node resource quantity before the update,

l̂_(sys)^((T))

is the total link resource quantity before the update, d_(max) andl_(max) are, respectively, an upper limit of allocable node resourcesand an upper limit of allocable link resources, t_(start) is aninstantiation time point of a to-be-accessed network slice correspondingto the access request, and T^(dur) is survival duration of theto-be-accessed network slice corresponding to the access request.

Compared with the prior art, the present disclosure has the followingbeneficial effects:

(1) The present disclosure includes: inputting the historical resourcedemand dataset of the network slice into the preset neural network fortraining; determining, based on the trained first neural network and thehistorical resource demand of the accessed network slice, the resourcedemand prediction information corresponding to the accessed networkslice in the first prediction time period, where the prediction timeperiod includes multiple time points, and the resource demand predictioninformation includes a resource demand prediction value corresponding toeach time point; and pre-allocating resources to the network slice basedon the resource demand prediction information and allocating resourcesto the network slice when a next time point arrives. In this way, theservice provider can reasonably allocate network resources for networkslices without violating the SLA, thus avoiding the waste of networkresources.

(2) The network resource pre-allocation algorithm proposed in thepresent disclosure can dynamically allocate node resources and linkresources for the network slices based on the resource demand predictioninformation, thereby avoiding waste caused by allocating redundantresources, improving resource utilization, and reducing resource costsof the network slices.

(3) The access control algorithm proposed in the present disclosure canassist access decision-making by using the prediction information toavoid waste of system resources caused by the overly conservative accesspolicy and reduce the waiting time of user requests.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic flowchart of a dynamic network resource allocationmethod based on network slicing according to an embodiment of thepresent disclosure.

FIG. 2 is a schematic structural diagram of an encoder-decoder longshort-term memory (LSTM) network according to an embodiment of thepresent disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The technical solutions in the embodiments of this application areclearly and completely described below with reference to the drawings inthe embodiments of this application. It will become apparent that thedescribed embodiments are merely some, rather than all, of theembodiments of this application. All other embodiments obtained by thoseof ordinary skill in the art based on the embodiments of the presentdisclosure without creative efforts should fall within the protectionscope of the present disclosure.

As described in the background art, in the existing technology, theservice provider cannot efficiently allocate network resources fornetwork slices without violating the SLA.

Therefore, this application proposes a dynamic network resourceallocation method based on network slicing. FIG. 1 is a schematicflowchart of a dynamic network resource allocation method based onnetwork slicing according to an embodiment of this application. Themethod includes the following steps:

S1: Input a historical resource demand dataset of an accessed networkslice into a first neural network for training.

In a specific application scenario, the first preset neural network isspecifically an encoder-decoder LSTM network with preference (LSTM-P),and the structure is shown in FIG. 2 . Each LSTM unit consists of 100neurons, LSTM units in the same layer are the same LSTM unit atdifferent moments, and the encoder consists of two LSTM units. Theresource demand history information is input, and a hidden state of thesecond LSTM unit at the last moment of a history window T_(obs) isoutput. The adapter converts the encoder output into a decoder inputformat. The decoder consists of a single LSTM unit, and a hidden stateof the LSTM unit at each moment of a prediction window T_(pre) isoutput. A fully connected layer consists of a single neuron encapsulatedthrough TimeDistributed(·), which calculates resource demand predictioninformation based on the output data of the decoder and outputs theresource demand prediction information to the output layer.

In this embodiment of this application, a training process of the firstpredictive neural network, that is, the step S1, specifically includesthe following sub-steps:

S11: Input the historical resource demand dataset into the encoder ofthe first predictive neural network to obtain encoder output data.

S12: Input the encoder output data into the decoder of the firstpredictive neural network to obtain decoder output data.

S13: Input the decoder output data into the fully connected layer of thefirst predictive neural network to obtain a prediction sequence.

S14: Determine a prediction loss between the prediction sequence and areal value based on a loss function and adjust a parameter value of thefirst neural network based on the prediction loss.

S15: Treat step S11 to step S14 as one iteration and stop training afterreaching a preset number of iterations.

Specifically, the historical resource demand dataset

x = {x⁽¹⁾, x⁽²⁾, ⋯, x^((t)), ⋯, x^((T_(obs)))}

of the accessed network slice is input into the encoder of the LSTM-P,and

h₁^((t)) ←

LSTM(x^((t)), h₁^((t − 1)), c₁^((t − 1)))

and

h₂^((t)) = LSTM(h₁^((t)), h₂^((t − 1)), c₂^((t − 1)))

are calculated at each moment t by using the following formulas: Thefinal hidden state

h₂^((T_(obs)))

of the second LSTM unit, that is, the encoder output data, is output.The specific formula is:

$LSTM( {x^{(t)},h^{({t - 1})},c^{({t - 1})}} ) = \{ \begin{matrix}{f^{(t)} = \sigma( {W_{f} \cdot \lbrack {h^{({t - 1})},x^{(t)}} \rbrack + b_{f}} ),} \\{i^{(t)} = \sigma( {W_{i} \cdot \lbrack {h^{({t - 1})},x^{(t)}} \rbrack + b_{i}} ),} \\{{\widetilde{c}}^{(t)} = \tanh( {W_{c} \cdot \lbrack {h^{({t - 1})},x^{(t)}} \rbrack + b_{c}} ),} \\{c^{(t)} = f^{(t)} \times c^{({t - 1})} + i^{(t)} \times {\widetilde{c}}^{(t)},} \\{o^{(t)} = \sigma( {W_{o} \cdot \lbrack {h^{({t - 1})},x^{(t)}} \rbrack + b_{o}} ),} \\{h^{(t)} = o^{(t)} \times \tanh( c^{(t)} ).}\end{matrix} )$

The adapter converts the encoder output h₂ ^((Tobs)) into the decoderinput format and then inputs it into the decoder. Assuming that

t = 1, ⋯, T_(pre), h₃^((t)) ← LSTM (h₂^((T_(obs))), h₃^((t − 1)), c₃^((t − 1)))

is calculated by using an objective function. The hidden state of theLSTM unit, that is, the decoder output data, at each moment of 1 toT_(pre) is output, that is,

h₃ = (h₃⁽¹⁾, h₃⁽²⁾, ..., h₃^((T_(pre)))).

The decoder output h₃ is used as the input of the fully connected layer,and ŷ ← W_(F) • h₃ + b_(F) is calculated. W_(F) and b_(F) are neuralnetwork parameters of the fully connected layer, and specific values areobtained from the training process. The prediction sequence

ŷ = {ŷ⁽¹⁾, ŷ⁽²⁾, ..., ŷ^((t)), ...ŷ^((T_(pre)))}

is output.

At the moment t_(p), when the LSTM-P is used to predict node resourcedemand of an accessed network slice i, the sequence

x = d_(i)^(Q) = {d_(i)^(Q(t_(p) − T_(obs) + 1)), d_(i)^(Q(t_(p) − T_(obs) + 2)), ..., d_(i)^(Q(t_(p)))}

is input and the sequence

ŷ = d̂_(i) = {d̂_(i)^((t_(p) + 1)), d̂_(i)^((t_(p) + 2)), ..., d̂_(i)^((t_(p) + T_(pre)))}

is output. When the LSTM-P is used to predict link resource demand ofthe accessed network slice i, the sequence

x = l_(i)^(Q) = {l_(i)^(Q(t_(p) − T_(obs) + 1)), l_(i)^(Q(t_(p) − T_(obs) + 2)), ⋯, l_(i)^(Q(t_(p)))}

is input, and the sequence

ŷ = l̂_(i) = {l̂_(i)^((t_(p) + 1)), l̂_(i)^((t_(p) + 2)), ⋯, l̂_(i)^((t_(p) + T_(pre)))}

is output.

When a resource demand prediction value ŷ^((t)) is higher than the realvalue y^((t)), the service provider allocates excessive resources to thenetwork slice based on the prediction information, resulting inadditional resource overheads. When the resource demand prediction valueis lower than the real value, the service provider allocates fewerresources to the network slice than the real demand based on theprediction information, violating the SLA and resulting in high defaultpayment. The following loss function is designed based on thecharacteristics of network slicing services to reflect the economic lossto the service provider from resource over-allocation and SLA violation.

$L( {y,\hat{y}} ) = \frac{1}{T_{pre}}\sum_{t = 1}^{T_{pre}}\mathcal{l}( {y^{(t)},{\hat{y}}^{(t)}} )$

and

$\mathcal{l}( {y^{(t)},{\hat{y}}^{(t)}} ) = \{ \begin{matrix}{{\hat{y}}^{(t)} - y^{(t)},} & {\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} if\mspace{6mu}{\hat{y}}^{(t)} > y^{(t)}} \\{\beta( {y^{(t)} - {\hat{y}}^{(t)}} ),} & {if{\hat{y}}^{(t)} \leq y^{(t)}}\end{matrix} )$

β>1 indicates the penalty strength of SLA violation for the serviceprovider, y is the real value, and y is the predicted value.

The service provider uses the historical resource usage data of thenetwork slice as the training set, and

L(y, ŷ)

as the loss function for the training process. The predictor LSTM-P istrained by using the gradient descent method or its variants. The neuralnetwork parameter is adjusted and the loss function value is reducedduring the iterative training process to ensure the high accuracy of thepredictor and low SLA violation rate.

The constructed LSTM-P predictor is used to implement resourcepre-allocation for active slices in the network slicing system and toperform access control for pending slice requests in the waiting queueto optimize resource utilization.

It should be noted that the above neural network is only a specificimplementation in this application, and those skilled in the art canflexibly choose a neural network with different configurations accordingto the actual situation. This does not affect the protection scope ofthis application.

S2: Determine, based on a trained first neural network and thehistorical resource demand of the accessed network slice, resourcedemand prediction information corresponding to the accessed networkslice in a first prediction time period.

In this embodiment of this application, the resource demand predictioninformation specifically includes a predicted node resource quantity anda predicted link resource quantity.

S3: Pre-allocate resources to the accessed network slice based on theresource demand prediction information and allocate resources to theaccessed network slice when the first prediction time period arrives.

In this embodiment of this application, the step of pre-allocatingresources to the accessed network slice specifically includesdetermining a pre-allocated node resource quantity and a pre-allocatedlink resource quantity of the accessed network slice in the firstprediction time period according to the following formulas:

${\overset{˙}{d}}_{i}^{(t)} = \gamma_{d}{\hat{d}}_{i}^{(t)},{\overset{˙}{l}}_{i}^{(t)} = \gamma_{l}{\hat{l}}_{i}^{(t)},\forall i \in \lbrack {1,n_{t}} \rbrack,$

where

$\gamma_{d} = \{ \underset{\frac{D}{\Sigma_{i = 1}^{n_{t}}\mspace{6mu}{\hat{d}}_{i}^{(t)}},\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} otherwise}{1 + {\gamma^{\prime}}_{d},\mspace{6mu} if\mspace{6mu}\Sigma_{i = 1}^{n_{t}}\mspace{6mu}{\gamma^{\prime}}_{d}{\hat{d}}_{i}^{(t)} < D} )\mspace{6mu},$

$\gamma_{d} = \{ \underset{\frac{L}{\Sigma_{i = 1}^{n_{t}}\mspace{6mu}{\hat{l}}_{i}^{(t)}},\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu} otherwise}{1 + {\gamma^{\prime}}_{i},\mspace{6mu} if\mspace{6mu}\Sigma_{i = 1}^{n_{t}}\mspace{6mu}{\gamma^{\prime}}_{l}{\hat{l}}_{i}^{(t)} < L} )\mspace{6mu},\,$

where

${\overset{˙}{d}}_{i}^{(t)}$

is a pre-allocated node resource quantity of an accessed network slice iin a first prediction time period t,

i_(i)^((t))

is a pre-allocated link resource quantity of the accessed network slicei in the first prediction time period t,

d̂_(i)^((t))

is a predicted node resource quantity of the accessed network slice i inthe first prediction time period t,

l̂_(i)^((t))

is a predicted link resource quantity of the accessed network slice i inthe first prediction time period t, n_(t) is a quantity of accessednetwork slices in a system in the first prediction time period t, γ_(d)≥ 1 represents node resource redundancy, γ_(l) ≥ 1 represents linkresource redundancy,

γ^(′)_(d) ≥ 0

and

γ^(′)_(i) ≥ 0

are offsets of corresponding predicted values, D is a total noderesource quantity of the system, and L is a total link resource quantityof the system.

In this embodiment of this application, the method further includes thefollowing steps:

A1: Receive access requests of all to-be-accessed network slices inreal-time and store all the access requests in a request queue.

A2: Determine a total node resource quantity and a total link resourcequantity that are occupied by all accessed network slices in a secondprediction time period.

A3: When a preset access time point arrives, sequentially decide whetherto allow the access requests in the request queue in the secondprediction time period; if yes, allow a corresponding access requestwhen the second prediction time period arrives; and if not, continue todecide whether to allow a next access request until all the accessrequests in the request queue are completely decided.

In a specific application scenario, the access requests of theto-be-accessed network slices, that is, network slice requests fromusers, are received in real-time and stored in the request queue. Inaddition, the access time point is preset in this application, and thenthe total node resource quantity and the total link resource quantitythat are occupied by all the accessed network slices in the secondprediction time period are determined first to decide whether to allowthe access requests.

In this embodiment of this application, the step of determining thetotal node resource quantity and the total link resource quantity in thestep A2 is specifically performed by using the following formulas:

${\hat{d}}_{sys}^{(T)} = {\sum_{i = 1}^{n_{T}}{\overset{˙}{d}}_{i}^{(T)}}\mspace{6mu},{\hat{l}}_{sys}^{(T)} = {\sum_{i = 1}^{n_{T}}{\overset{˙}{l}}_{i}^{(T)}}\mspace{6mu},\forall T\mspace{6mu} \in \mspace{6mu}\mspace{6mu}\lbrack {T_{ac} + 1,\mspace{6mu} T_{ac} + T_{sys}} \rbrack,$

where

d̂_(sys)^((T))

is the total node resource quantity,

l̂_(sys)^((T))

is the total link resource quantity, T_(sys) is a length of a systemresource demand prediction window,

${\overset{˙}{d}}_{i}^{(T)}$

and

${\overset{˙}{l}}_{i}^{(T)}$

are, respectively, a node resource quantity and a link resource quantityto be occupied by an accessed network slice i at a moment T in thesecond prediction time period [T_(ac) + 1, T_(ac) + T_(sys)], T_(ac) isthe preset access time point, and n_(T) is a quantity of accessednetwork slices in the system at the moment T in the second predictiontime period.

Specifically, when the total node resource quantity and the total linkresource quantity are determined, first, a node resource quantity and alink resource quantity that are occupied by each accessed network slicein the second prediction time period are determined, and then the noderesource quantities and the link resource quantities occupied by all theaccessed network slices in the second prediction time period are summed,respectively.

It should be noted that the node resource quantity and the link resourcequantity that are occupied by the accessed network slice in the secondprediction time period are specifically determined by using a secondneural network, which is the same as the first neural network, that is,the LSTM. The prediction formula is also the same as the first neuralnetwork.

In this embodiment of this application, all the network slices areclassified into high-quality network slices and low-quality networkslices.

In this embodiment of this application, the step of deciding whether toallow the access requests in the second prediction time period in thestep A3 is specifically performed by using the following formulas:

$\begin{array}{l}{\exists t_{start} \in \lbrack {T_{ac} + 1,T_{ac} + T_{adm}} \rbrack,\forall T \in \lbrack {t_{start,}t_{start} + T^{dur}} \rbrack,{\hat{d}}_{sys}^{(T)} + d_{max} \leq} \\{D,{\hat{l}}_{sys}^{(T)} + l_{max} \leq L,}\end{array}$

where t_(start) is an instantiation time point of a to-be-accessednetwork slice corresponding to the access request, T_(ac) is adecision-making time point,

d̂_(sys)^((T))

and

l̂_(sys)^((T))

are, respectively, a total node resource quantity and a total linkresource quantity that are occupied by all the network slices at themoment T in the second prediction time period, T_(adm) is an accesswindow dimension, T^(dur) is survival duration of the to-be-accessednetwork slice corresponding to the access request, d_(max) and l_(max)are, respectively, an upper limit of allocable node resources and anupper limit of allocable link resources; if a user requests ahigh-quality network slice, then

⟨d_(max), l_(max)⟩ = ⟨d_(max)^(H), l_(max)^(H)⟩

; if the user requests a standard quality network slice, then

$\begin{array}{l}{\langle {d_{m ax},l_{\max}^{}} \rangle = \langle {d_{m ax}^{S},l_{\max}^{S}} \rangle} \\

\end{array}$

; D is a total node resource quantity of the system, L is a total linkresource quantity of the system,

d_(max)^(H)

is an upper limit of node resources for the high-quality network slices,

l_(max)^(H)

is an upper limit of link resources for the high-quality network slices,

d_(max)^(S)

is an upper limit of node resources for standard quality network slices,and

l_(max)^(S)

is an upper limit of link resources for the standard quality networkslices.

Specifically, the upper limit is a maximum resource quantityartificially set for each slice, and the resources occupied by eachslice are limited in a specific range to avoid a slice from occupyingmost of the system resources.

The total node resource quantity D of the system is specifically a totalnode resource quantity available to the service provider, that is, thetotal node resource quantity in the system that can be allocated to thenetwork slices. The total link resource quantity L of the system isspecifically a total link resource quantity available to the serviceprovider, that is, the total link resource quantity in the system thatcan be allocated to the network slices.

In this embodiment of this application, in the step A3, each time anaccess request is decided to be allowed in the second prediction timeperiod, the total node resource quantity and the total link resourcequantity are updated instantaneously, and the next access request isdecided after the total node resource quantity and the total linkresource quantity are updated.

In this embodiment of this application, the total node resource quantityand the total link resource quantity are updated by using the followingformulas:

d̂_(sys)^((TT)) = d̂_(sys)^((T)) = d_(max), l̂_(sys)^((TT)) = l̂_(sys)^((T)) + l_(max), ∀T ∈ [t_(start), t_(start) + T^(dur)],

where

d̂_(sys)^((TT))

is an updated total node resource quantity,

l̂_(sys)^((TT))

is an updated total link resource quantity,

d̂_(sys)^((T))

is the total node resource quantity before the update,

l̂_(sys)^((T))

is the total link resource quantity before the update, d_(max) andl_(max) are, respectively, an upper limit of allocable node resourcesand an upper limit of allocable link resources, t_(start) is aninstantiation time point of a to-be-accessed network slice correspondingto the access request, and T^(dur) is survival duration of theto-be-accessed network slice corresponding to the access request.

Specifically, when the next access request is decided, indicators of theupdated total node resource quantity and the updated total link resourcequantity are changed to the indicators of the total node resourcequantity and the total link resource quantity before the update.

Those of ordinary skill in the art will understand that the embodimentsdescribed herein are intended to help readers understand the principlesof the present disclosure, and it should be understood that theprotection scope of the present disclosure is not limited to thespecific statements and embodiments of the present disclosure. Those ofordinary skill in the art may make other various specific modificationsand combinations according to the technical teachings disclosed in thepresent disclosure without departing from the essence of the presentdisclosure, and such modifications and combinations still fall withinthe protection scope of the present disclosure.

What is claimed is:
 1. A dynamic network resource allocation methodbased on network slicing, comprising: S1: inputting a historicalresource demand dataset of an accessed network slice into a first neuralnetwork for training; S2: determining, based on a trained first neuralnetwork and the historical resource demand of the accessed networkslice, resource demand prediction information corresponding to theaccessed network slice in a first prediction time period; and S3:pre-allocating resources to the accessed network slice based on theresource demand prediction information, and allocating resources to theaccessed network slice when the first prediction time period arrives;wherein the resource demand prediction information comprises a predictednode resource quantity and a predicted link resource quantity; whereinthe step of pre-allocating resources to the accessed network slice inthe step S3 comprises determining a pre-allocated node resource quantityand a pre-allocated link resource quantity of the accessed network slicein the first prediction time period according to the following formulas:${\overset{˙}{d}}_{i}^{(t)} = \gamma_{d}{\hat{d}}_{i}^{(t)},{\overset{˙}{l}}_{\overset{˙}{l}}^{(t)} = \gamma_{l}{\hat{l}}_{\overset{˙}{l}}^{(t)},\forall i \in \lbrack {1,n_{t}} \rbrack,$wherein if${\sum_{i = 1}^{n_{t}}{{\gamma^{\prime}}_{d}{\hat{d}}_{i}^{(t)}}} < D,\text{then}\mspace{6mu}\gamma_{d} = 1 + {\gamma^{\prime}}_{d,}\mspace{6mu}\text{otherwise,}\mspace{6mu}\gamma_{d} = \frac{D}{\sum_{i = 1}^{n_{t}}{\hat{d}}_{i}^{(t)}};\text{and}$$\text{if}{\sum_{i = 1}^{n_{t}}{{\gamma^{\prime}}_{l}{\hat{l}}_{i}^{(t)}}} < L,\,\text{then}\,\gamma_{l} = 1 + {\gamma^{\prime}}_{l},\text{otherwise,}\,\,\gamma_{l} = \frac{L}{\sum_{i = 1}^{n_{t}}{\hat{l}}_{i}^{(t)}};$wherein ${\overset{˙}{d}}_{i}^{(t)}$ is a pre-allocated node resourcequantity of an accessed network slice i in a first prediction timeperiod t, ${\overset{˙}{l}}_{\overset{˙}{l}}^{(t)}$ is a pre-allocatedlink resource quantity of the accessed network slice i in the firstprediction time period t, d̂_(i)^((t)) is a predicted node resourcequantity of the accessed network slice i in the first prediction timeperiod t, l̂_(i)^((t)) is a predicted link resource quantity of theaccessed network slice i in the first prediction time period t, n_(t) isa quantity of accessed network slices in a system in the firstprediction time period t, γ_(d) ≥ 1 represents node resource redundancy,γ_(l) ≥ 1 represents link resource redundancy, γ^(′)_(d) ≥ 0 andγ^(′)_(l) ≥ 0 are offsets of corresponding predicted values, D is atotal node resource quantity of the system, and L is a total linkresource quantity of the system.
 2. The dynamic network resourceallocation method based on network slicing according to claim 1, furthercomprising: A1: receiving access requests of all to-be-accessed networkslices in real-time and storing all the access requests in a requestqueue; A2: determining a total node resource quantity and a total linkresource quantity that are occupied by all accessed network slices in asecond prediction time period; and A3: when a preset access time pointarrives, sequentially deciding whether to allow the access requests inthe request queue in the second prediction time period; if yes, allowinga corresponding access request when the second prediction time periodarrives; and if not, continuing to decide whether to allow a next accessrequest until all the access requests in the request queue arecompletely decided.
 3. The dynamic network resource allocation methodbased on network slicing according to claim 2, wherein the step ofdetermining the total node resource quantity and the total link resourcequantity in the step A2 is performed by using the following formulas:${\hat{d}}_{sys}^{(T)} = {\sum_{i = 1}^{n_{T}}{\overset{˙}{d}}_{sys}^{(T)}},{\hat{l}}_{sys}^{(T)} = {\sum_{i = 1}^{n_{T}}{\overset{˙}{l}}_{i}^{(T)}},\forall T \in \lbrack {T_{ac} + 1,T_{ac} + T_{sys}} \rbrack,$wherein d̂_(sys)^((T)) is the total node resource quantity, l̂_(sys)^((T))is the total link resource quantity, T_(sys) is a length of a systemresource demand prediction window,${\overset{˙}{d}}_{i}^{(T)}\mspace{6mu}\text{and}\mspace{6mu}{\overset{˙}{l}}_{i}^{(T)}$are, respectively, a node resource quantity and a link resource quantityto be occupied by an accessed network slice i at a moment T in thesecond prediction time period [T_(ac) + 1, T_(ac) + T_(sys)], T_(ac) isthe preset access time point, and n_(T) is a quantity of accessednetwork slices in the system at the moment T in the second predictiontime period.
 4. The dynamic network resource allocation method based onnetwork slicing according to claim 2, wherein all the network slices areclassified into high-quality network slices and low-quality networkslices.
 5. The dynamic network resource allocation method based onnetwork slicing according to claim 4, wherein the step of decidingwhether to allow the access requests in the second prediction timeperiod in the step A3 is performed by using the following formulas:$\begin{array}{l}{\exists t_{start} \in \lbrack {T_{ac} + 1,T_{ac} + T_{adm}} \rbrack,\forall T \in \lbrack {t_{start},t_{start} + T^{dur}} \rbrack,} \\{{\hat{d}}_{sys}^{(T)} + d_{max} \leq D,{\hat{l}}_{sys}^{(T)} + l_{max} \leq L,}\end{array}$ wherein t_(start) is an instantiation time point of ato-be-accessed network slice corresponding to the access request, T_(ac)is a decision-making time point, d̂_(sys)^((T)) and l̂_(sys)^((T)) are,respectively, a total node resource quantity and a total link resourcequantity that are occupied by all the network slices at the moment T inthe second prediction time period, T_(adm) is an access windowdimension, T^(dur) is survival duration of the to-be-accessed networkslice corresponding to the access request, d_(max) and l_(max) are,respectively, an upper limit of allocable node resources and an upperlimit of allocable link resources; if a user requests a high-qualitynetwork slice, then ⟨d_(max), l_(max)⟩ = ⟨d_(max)^(H), l_(max)^(H)⟩; ifthe user requests a standard quality network slice, then⟨d_(max), l_(max)⟩ = ⟨d_(max)^(S), l_(max)^(S)⟩; D is a total noderesource quantity of the system, L is a total link resource quantity ofthe system, d_(max)^(H) is an upper limit of node resources for thehigh-quality network slices, l_(max)^(H) is an upper limit of linkresources for the high-quality network slices, d_(max)^(S) is an upperlimit of node resources for standard quality network slices, andl_(max)^(S) is an upper limit of link resources for the standard qualitynetwork slices.
 6. The dynamic network resource allocation method basedon network slicing according to claim 3, wherein in the step A3, eachtime an access request is decided to be allowed in the second predictiontime period, the total node resource quantity and the total linkresource quantity are updated instantaneously, and a next access requestis decided after the total node resource quantity and the total linkresource quantity are updated.
 7. The dynamic network resourceallocation method based on network slicing according to claim 6, whereinthe total node resource quantity and the total link resource quantityare updated by using the following formulas:d̂_(sys)^((TT)) = d̂_(sys)^((T)) + d_(max), l̂_(sts)^((TT)) = l̂_(sys)^((T)) + l_(max), ∀T ∈ [t_(start), t_(start) + T^(dur)],wherein d̂_(sys)^((TT)) is an updated total node resource quantity,l̂_(sys)^((TT)) is an updated total link resource quantity, d̂_(sys)^((T))is the total node resource quantity before update, l̂_(sys)^((T)) is thetotal link resource quantity before update, d_(max) and l_(max) are,respectively, an upper limit of allocable node resources and an upperlimit of allocable link resources, t_(start) is an instantiation timepoint of a to-be-accessed network slice corresponding to the accessrequest, and T^(dur) is survival duration of the to-be-accessed networkslice corresponding to the access request.